Multiview Hessian discriminative sparse coding for image annotation

  • Authors:

  • Weifeng Liu;Dacheng Tao;Jun Cheng;Yuanyan Tang

  • Affiliations:

  • College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, Shandong, China;Centre for Quantum Computation & Intelligent Systems and Faculty of Engineering & Information Technology, University of Technology, Sydney, Ultimo, NSW 2007, Australia;Shenzhen Key Lab for CVPR, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China and The Chinese University of Hong Kong, Sha Tin, Hong Kong, China;Faculty of Science and Technology, University of Macau, Taipa, Macau, China

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2014

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Abstract

Sparse coding represents a signal sparsely by using an overcomplete dictionary, and obtains promising performance in practical computer vision applications, especially for signal restoration tasks such as image denoising and image inpainting. In recent years, many discriminative sparse coding algorithms have been developed for classification problems, but they cannot naturally handle visual data represented by multiview features. In addition, existing sparse coding algorithms use graph Laplacian to model the local geometry of the data distribution. It has been identified that Laplacian regularization biases the solution towards a constant function which possibly leads to poor extrapolating power. In this paper, we present multiview Hessian discriminative sparse coding (mHDSC) which seamlessly integrates Hessian regularization with discriminative sparse coding for multiview learning problems. In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation. We conduct extensive experiments on PASCAL VOC'07 dataset and demonstrate the effectiveness of mHDSC for image annotation.